Healthcare AI Adoption Hindered by Scattered Patient Data
Experts reveal challenges in healthcare AI adoption due to scattered patient data.
Key Points
- • AI adoption in healthcare is slow due to scattered patient data.
- • Unified systems are necessary for effective AI implementation.
- • AI chatbots are increasingly improving customer service in healthcare.
- • Real-time data access is critical for AI functionality in healthcare.
On July 20, 2025, experts highlighted ongoing challenges in the healthcare industry regarding the implementation of artificial intelligence (AI) solutions due to the fragmentation of patient data across multiple systems. According to Kevin Deutsch, Senior Vice President of Health Plans at Softheon, effective AI adoption is hindered as healthcare organizations struggle to unify their data, leading to a slow integration of these technologies into patient care strategies.
The complexity of scattered patient data not only delays AI initiatives but also limits the potential for delivering personalized member experiences, which is a growing demand among healthcare consumers. Deutsch emphasized the necessity for robust data governance to harness AI's full potential, stating, "While there is significant discussion around AI, many health plans lack a clear strategy for its implementation, especially in enhancing member experiences."
To address these data challenges, experts urge healthcare organizations to take inventory of their existing data systems before embarking on AI projects. Softheon is actively involved in developing AI solutions that span algorithmic, generative, and agentic forms, facilitating improved operational efficiencies. Deutsch noted that adopting AI chatbots for routine customer inquiries has already started to transform customer service within healthcare, allowing human agents to focus on more complex issues.
Furthermore, the shift to real-time data access is critical as it allows healthcare organizations to maintain up-to-date information for their AI applications. Deutsch pointed out that traditional Electronic Data Interchange (EDI) systems are becoming insufficient, with real-time data streaming seen as essential in rapidly changing healthcare environments. He advised health plans to prioritize understanding their data landscape to centralize access, as out-of-date information could severely limit AI effectiveness.
Looking forward, Deutsch predicts a general blurring of lines between human and AI interactions, suggesting that consumers may not be concerned whether they interact with AI or human agents, provided their needs are effectively met. He concluded, "The focus should be on identifying and solving specific problems with AI rather than adopting it for its own sake."